Virtual Character Animation based on Data-driven Motion Capture using Deep Learning Technique

Rajendran, Gopika; Lee, Ojus Thomas

doi:10.1109/iciis51140.2020.9342693

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Several methods have been proposed to solve the automatic rigging of meshes. They rely on algorithms that process the geometric structure [Jacobson et al 2012] or, more recently, by learning using data-driven approaches [Rajendran and Lee 2020]. However, they hardly produce coherent results among arbitrary objects and do not provide the user with additional information about the semantics and the structural role of each component of the shape.…”

Section: Introductionmentioning

confidence: 99%

A functional skeleton transfer

Musoni¹,

Marin²,

Melzi³

et al. 2021

Preprint

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Section: Introductionmentioning

confidence: 99%

A functional skeleton transfer

Musoni¹,

Marin²,

Melzi³

et al. 2021

Preprint

View full text Add to dashboard Cite

A functional skeleton transfer

Musoni

Marin

Melzi

et al. 2021

Proc. ACM Comput. Graph. Interact. Tech.

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The animation community has spent significant effort trying to ease rigging procedures. This is necessitated because the increasing availability of 3D data makes manual rigging infeasible. However, object animations involve understanding elaborate geometry and dynamics, and such knowledge is hard to infuse even with modern data-driven techniques. Automatic rigging methods do not provide adequate control and cannot generalize in the presence of unseen artifacts. As an alternative, one can design a system for one shape and then transfer it to other objects. In previous work, this has been implemented by solving the dense point-to-point correspondence problem. Such an approach requires a significant amount of supervision, often placing hundreds of landmarks by hand. This paper proposes a functional approach for skeleton transfer that uses limited information and does not require a complete match between the geometries. To do so, we suggest a novel representation for the skeleton properties, namely the functional regressor, which is compact and invariant to different discretizations and poses. We consider our functional regressor a new operator to adopt in intrinsic geometry pipelines for encoding the pose information, paving the way for several new applications. We numerically stress our method on a large set of different shapes and object classes, providing qualitative and numerical evaluations of precision and computational efficiency. Finally, we show a preliminar transfer of the complete rigging scheme, introducing a promising direction for future explorations.

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Humans in (Digital) Space

Lycett

Reppel

2022

Proceedings of the 2022 International Conference on Advanced Visual Interfaces

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Virtual Character Animation based on Data-driven Motion Capture using Deep Learning Technique

Cited by 3 publications

References 13 publications

A functional skeleton transfer

A functional skeleton transfer

A functional skeleton transfer

Humans in (Digital) Space

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